Face detection has a variety of applications in government, security, etc. Face detection involves the ability of software and apparatus to examine a composite image and detect the presence of faces within the image. Face detection algorithms are known in the art and are computationally expensive. A variety of techniques utilized in prior art face detection algorithms are described and cited as references 1–28 in the '596 provisional application. One of the main challenges in face detection technology is the ability to discriminate between face and non face images given that the faces may be of any size and located anywhere in the image.
Earlier efforts have been focused on correlation or template matching, matched filtering, subset methods, deformable templates, and similar such techniques. One representative technique, in order to cope with the variability of face images, utilizes six Gaussian clusters to model the probability distributions for face and non-face patterns, respectively. The density functions of the Gaussian distributions are then fed through a multilayer perceptron classifier for face detection. Such techniques are extremely computationally expensive, and subject to relatively high rates of false detection.
It is an object of the invention to decrease the computational cost of performing face detection;
It is also an object of the invention to provide a face detection algorithm that has an acceptable false detection;
It is also an object of the invention to diminish the large number of probability density functions utilized in face detection algorithms;
It is still a further object of the present invention to provide a relatively accurate face detection algorithm.